Download or read book Urban Area Traffic Flow Forecasting in Intelligent Transportation Systems written by Ziyue Wang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Currently, Intelligent Transportation Systems (ITS), is revolutionizing the transportation industry. ITS incorporates advanced Internet of Things (IoT) technologies to implement "Smart City". These technologies produce tremendous amount of real time data from diverse sources that can be used to solve transportation problems. In this thesis, I focus on one such problem, traffic congestion in urban areas. A road segment affected by traffic affects the surrounding road segments. This is obvious. However, over a period of time, other roads not necessarily close in proximity to the congested road segment may also be affected. The congestion is not stationary. It is dynamic and it spreads. I address this issue by first formulating a similarity function using ideas from network theory. Using this similarity function, I then cluster the road points affected by traffic using affinity propagation clustering, a distributed message passing algorithm. Finally, I predict the effect of traffic on this cluster using long-short term memory neural network model. I evaluate and show the feasibility of my proposed clustering and prediction algorithm during peak and non-peak hours on open source traffic data set.

Download or read book Advanced Intelligent Predictive Models for Urban Transportation written by R. Sathiyaraj and published by CRC Press. This book was released on 2022-03-27 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.

Download or read book Traffic Flow Forecasting for Intelligent Transportation Systems written by Brian L. Smith and published by . This book was released on 1995 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: The capability to forecast traffic volume in an operational setting has been identified as a critical need for intelligent transportation systems (ITS). In particular, traffic volume forecasts will directly support proactive traffic control and accurate travel time estimation. However, previous attempts to develop traffic volume forecasting models have met with limited success. This research focused on developing such models for two sites on the Capital Beltway in Northern Virginia. Four models were developed and tested for the single-interval forecasting problem, which is defined as estimating traffic flow 15 min into the future. The four models were the historical average, time series, neural network, and nonparametric regression models. The nonparametric regression model significantly outperformed the others. Based on its success on the single-interval forecasting problem, the nonparametric regression approach was used to develop and test a model for the multiple-interval forecasting problem. This problem is defined as estimating traffic flow for a series of time periods into the future in 15-min intervals. The model performed well in this application. In general, the model was portable, accurate, and easy to deploy in a field environment. Finally, an ITS system architecture was developed to take full advantage of the forecasting capability. The architecture illustrates the potential for significantly improved ITS services with enhanced analysis components, such as traffic volume forecasting.

Download or read book Traffic Information and Control written by Ruimin Li and published by Institution of Engineering and Technology. This book was released on 2020-11-16 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by an international team of researchers, this book focuses on traffic information processing and signal control using emerging types of traffic data. It conveys advanced methods to estimate and predict traffic flows at different levels, including macroscopic, mesoscopic and microscopic. The aim of these predictions is to optimize traffic signal control for intersections and to mitigate ever-growing traffic congestion.

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio and published by Now Publishers Inc. This book was released on 2009 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Download or read book Present Approach to Traffic Flow Theory and Research in Civil and Transportation Engineering written by Elżbieta Macioszek and published by Springer Nature. This book was released on 2022-01-03 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents many valuable tips for making decisions related to traffic flow in the transport networks. The knowledge base in practical examples, as well as the decision support systems described in this book, finds interest among people who face the daily challenge of searching for solutions to the problems of contemporary transport networks and systems. The publication is therefore addressed to local authorities related to the planning and development of development strategies for selected areas with regard to transport (both in the urban and regional dimension) and to representatives of business and industry, as people directly involved in the implementation of traffic engineering solutions. The tips contained in individual sections of the publication allow to look at a given problem in an advanced way and facilitate the selection of the appropriate strategy (among others, in relation to the evaluation of BEV and FCHEV electric vehicles in the creation of a sustainable transport systems, development of ecological public transport on the example of selected cities, impact of drivers' waiting time on the gap acceptance at median, uncontrolled T-intersections). In turn, due to a new approach to theoretical models (including, inter alia, the application of genetic algorithms for the planning of urban rail transportation system, comprehensive estimate of life cycle costs of new technical systems using reliability verification algorithm, application and comparison of machine learning algorithms in traffic signals prediction), the publication also interests scientists and researchers carrying out research in this area.

Download or read book Intelligent Transportation Systems written by Ahmed Abdel-Rahim and published by BoD – Books on Demand. This book was released on 2012-03-16 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Transportation Systems (ITS) have transformed surface transportation networks through the integration of advanced communications and computing technologies into the transportation infrastructure. ITS technologies have improved the safety and mobility of the transportation network through advanced applications such as electronic toll collection, in-vehicle navigation systems, collision avoidance systems, and advanced traffic management systems, and advanced traveler information systems. In this book that focuses on different ITS technologies and applications, authors from several countries have contributed chapters covering different ITS technologies, applications, and management practices with the expectation that the open exchange of scientific results and ideas presented in this book will lead to improved understanding of ITS technologies and their applications.

Download or read book Inter urban Short term Traffic Congestion Prediction written by Giovanni Huisken and published by . This book was released on 2006 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook on Artificial Intelligence and Transport written by Hussein Dia and published by Edward Elgar Publishing. This book was released on 2023-10-06 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.

Download or read book Forecasting Freeway Traffic Flow for Intelligent Transportation Systems Application written by Brian L. Smith and published by . This book was released on 1995 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mobility Data Driven Urban Traffic Monitoring written by Zhidan Liu and published by Springer Nature. This book was released on 2021-05-18 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.

Download or read book Large scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility written by Arief Koesdwiady and published by . This book was released on 2018 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing and developing a new generation of cities around the world (termed as smart cities) is fast becoming one of the ultimate solutions to overcome cities' problems such as population growth, pollution, energy crisis, and pressure demand on existing transportation infrastructure. One of the major aspects of a smart city is smart mobility. Smart mobility aims at improving transportation systems in several aspects: city logistics, info-mobility, and people-mobility. The emergence of the Internet of Car (IoC) phenomenon alongside with the development of Intelligent Transportation Systems (ITSs) opens some opportunities in improving the traffic management systems and assisting the travelers and authorities in their decision-making process. However, this has given rise to the generation of huge amount of data originated from human-device and device-device interaction. This is an opportunity and a challenge, and smart mobility will not meet its full potential unless valuable insights are extracted from these big data. Although the smart city environment and IoC allow for the generation and exchange of large amounts of data, there have not been yet well de ned and mature approaches for mining this wealth of information to benefit the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). The proposed framework is focused around four main aspects that tackle major data-driven traffic flow prediction problems: the fusion of hard data for traffic flow prediction; the fusion of soft data for traffic flow prediction; prediction of non-stationary traffic flow; and prediction of multi-step traffic flow. All these aspects are investigated and formulated as computational based tools/algorithms/approaches adequately tailored to the nature of the data at hand. The first tool tackles the inherent big data problems and deals with the uncertainty in the prediction. It relies on the ability of deep learning approaches in handling huge amounts of data generated by a large-scale and complex transportation system with limited prior knowledge. Furthermore, motivated by the close correlation between road traffic and weather conditions, a novel deep-learning-based approach that predicts traffic flow by fusing the traffic history and weather data is proposed. The second tool fuses the streams of data (hard data) and event-based data (soft data) using Dempster Shafer Evidence Theory (DSET). One of the main features of the DSET is its ability to capture uncertainties in probabilities. Subsequently, an extension of DSET, namely Dempsters conditional rules for updating belief, is used to fuse traffic prediction beliefs coming from streams of data and event-based data sources. The third tool consists of a method to detect non-stationarities in the traffic flow and an algorithm to perform online adaptations of the traffic prediction model. The proposed detection approach is developed by monitoring the evolution of the spectral contents of the traffic flow. Furthermore, the approach is specfi cally developed to work in conjunction with state-of-the-art machine learning methods such as Deep Neural Network (DNN). By combining the power of frequency domain features and the known generalization capability and scalability of DNN in handling real-world data, it is expected that high prediction performances can be achieved. The last tool is developed to improve multi-step traffic flow prediction in the recursive and multi-output settings. In the recursive setting, an algorithm that augments the information about the current time-step is proposed. This algorithm is called Conditional Data as Demonstrator (C-DaD) and is an extension of an algorithm called Data as Demonstrator (DaD). Furthermore, in the multi-output setting, a novel approach of generating new history-future pairs of data that are aggregated with the original training data using Conditional Generative Adversarial Network (C-GAN) is developed. To demonstrate the capabilities of the proposed approaches, a series of experiments using artificial and real-world data are conducted. Each of the proposed approaches is compared with the state-of-the-art or currently existing approaches.

Download or read book Urban Traffic Networks written by Nathan H. Gartner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problems of urban traffic in the industrially developed countries have been at the top of the priority list for a long time. While making a critical contribution to the economic well being of those countries, transportation systems in general and highway traffic in particular, also have detrimental effects which are evident in excessive congestion, high rates of accidents and severe pollution problems. Scientists from different disciplines have played an important role in the development and refinement of the tools needed for the planning, analysis, and control of urban traffic networks. In the past several years, there were particularly rapid advances in two areas that affect urban traffic: 1. Modeling of traffic flows in urban networks and the prediction of the resulting equilibrium conditions; 2. Technology for communication with the driver and the ability to guide him, by providing him with useful, relevant and updated information, to his desired destination.

Download or read book Logic Driven Traffic Big Data Analytics written by Shaopeng Zhong and published by Springer Nature. This book was released on 2022-02-01 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.

Download or read book Smart Transportation Systems 2019 written by Xiaobo Qu and published by Springer. This book was released on 2019-06-06 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book gathers selected papers presented at the KES International Symposium on Smart Transportation Systems (KES-STS 2019). Modern transportation systems have undergone a rapid transformation in recent years. This has produced a range of vehicle technology innovations such as connected vehicles, self-driving cars, electric vehicles, Hyperloop, and even flying cars, and with them, fundamental changes in transport systems around the world. The book discusses current challenges, innovations and breakthroughs in Smart Transportation Systems, as well as transport infrastructure modeling, safety analysis, freeway operations, intersection analysis, and other related cutting-edge topics.

Download or read book Intelligent Vehicular Networks and Communications written by Anand Paul and published by Elsevier. This book was released on 2016-09-02 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions begins with discussions on how the transportation system has transformed into today’s Intelligent Transportation System (ITS). It explores the design goals, challenges, and frameworks for modeling an ITS network, discussing vehicular network model technologies, mobility management architectures, and routing mechanisms and protocols. It looks at the Internet of Vehicles, the vehicular cloud, and vehicular network security and privacy issues. The book investigates cooperative vehicular systems, a promising solution for addressing current and future traffic safety needs, also exploring cooperative cognitive intelligence, with special attention to spectral efficiency, spectral scarcity, and high mobility. In addition, users will find a thorough examination of experimental work in such areas as Controller Area Network protocol and working function of On Board Unit, as well as working principles of roadside unit and other infrastructural nodes. Finally, the book examines big data in vehicular networks, exploring various business models, application scenarios, and real-time analytics, concluding with a look at autonomous vehicles. Proposes cooperative, cognitive, intelligent vehicular networks Examines how intelligent transportation systems make more efficient transportation in urban environments Outlines next generation vehicular networks technology

Download or read book Explainable Artificial Intelligence for Intelligent Transportation Systems written by Loveleen Gaur and published by Springer Nature. This book was released on 2022-08-08 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.